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Purpose <p>To develop a high-performance machine learning model for predicting and interpreting features of pulmonary diseases.</p> Patients and methods <p>This retrospective study analyzed clinical and imaging data from patients with non-small cell lung cancer (NSCLC), granulomatous inflammation, and benign tumors, collected across multiple centers from January 2015 to October 2023. Data from two hospitals in Anhui Province were split into a development set (<i>n</i> = 1696) and a test set (<i>n</i> = 424) in an 8:2 ratio, with an external validation set (<i>n</i> = 909) from Zhejiang Province. Features with <i>p</i> &lt; 0.05 from univariate analyses were selected using the Boruta algorithm for input into Random Forest (RF) and XGBoost models. Model efficacy was assessed using receiver operating characteristic (ROC) analysis.</p> Results <p>A total of 3030 patients were included: 2269 with NSCLC, 529 with granulomatous inflammation, and 232 with benign tumors. The Obuchowski indices for RF and XGBoost in the test set were 0.7193 (95% CI: 0.6567–0.7812) and 0.8282 (95% CI: 0.7883–0.8650), respectively. In the external validation set, indices were 0.7932 (95% CI: 0.7572–0.8250) for RF and 0.8074 (95% CI: 0.7740–0.8387) for XGBoost. XGBoost achieved better accuracy in both the test (0.81) and external validation (0.79) sets. Calibration Curve and Decision Curve Analysis (DCA) showed XGBoost offered higher net clinical benefit.</p> Conclusion <p>The XGBoost model outperforms RF in the three-class classification of lung diseases.</p> Critical relevance statement <p>XGBoost surpasses Random Forest in accurately classifying NSCLC, granulomatous inflammation, and benign tumors, offering superior clinical utility via multicenter data.</p> Key Points <p><UnorderedList Mark="Bullet"> <ItemContent> <p>Lung cancer classification model has broad clinical applicability.</p> </ItemContent> <ItemContent> <p>XGBoost outperforms random forests using CT imaging data.</p> </ItemContent> <ItemContent> <p>XGBoost model can be deployed on a website for clinicians.</p> </ItemContent> </UnorderedList></p> Graphical Abstract <p></p>

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Development, deployment, and feature interpretability of a three-class prediction model for pulmonary diseases

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月26日

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  • Zhenyu Cao,
  • Gang Xu,
  • Yuan Gao,
  • Jianying Xu,
  • Fengjuan Tian,
  • Hengfeng Shi,
  • Dengfa Yang,
  • Zongyu Xie,
  • Jian Wang

摘要

Purpose

To develop a high-performance machine learning model for predicting and interpreting features of pulmonary diseases.

Patients and methods

This retrospective study analyzed clinical and imaging data from patients with non-small cell lung cancer (NSCLC), granulomatous inflammation, and benign tumors, collected across multiple centers from January 2015 to October 2023. Data from two hospitals in Anhui Province were split into a development set (n = 1696) and a test set (n = 424) in an 8:2 ratio, with an external validation set (n = 909) from Zhejiang Province. Features with p < 0.05 from univariate analyses were selected using the Boruta algorithm for input into Random Forest (RF) and XGBoost models. Model efficacy was assessed using receiver operating characteristic (ROC) analysis.

Results

A total of 3030 patients were included: 2269 with NSCLC, 529 with granulomatous inflammation, and 232 with benign tumors. The Obuchowski indices for RF and XGBoost in the test set were 0.7193 (95% CI: 0.6567–0.7812) and 0.8282 (95% CI: 0.7883–0.8650), respectively. In the external validation set, indices were 0.7932 (95% CI: 0.7572–0.8250) for RF and 0.8074 (95% CI: 0.7740–0.8387) for XGBoost. XGBoost achieved better accuracy in both the test (0.81) and external validation (0.79) sets. Calibration Curve and Decision Curve Analysis (DCA) showed XGBoost offered higher net clinical benefit.

Conclusion

The XGBoost model outperforms RF in the three-class classification of lung diseases.

Critical relevance statement

XGBoost surpasses Random Forest in accurately classifying NSCLC, granulomatous inflammation, and benign tumors, offering superior clinical utility via multicenter data.

Key Points

Lung cancer classification model has broad clinical applicability.

XGBoost outperforms random forests using CT imaging data.

XGBoost model can be deployed on a website for clinicians.

Graphical Abstract